Next Article in Journal
Channel-Based Network for Fast Object Detection of 3D LiDAR
Previous Article in Journal
Localization of Small Anomalies via the Orthogonality Sampling Method from Scattering Parameters
Previous Article in Special Issue
Robustness and Unpredictability for Double Arbiter PUFs on Silicon Data: Performance Evaluation and Modeling Accuracy
Open AccessArticle

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

1
College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT 0810, Australia
2
School of Computing and Informatics, University of Louisiana at Lafayette, Louisiana, LA 70504, USA
3
School of Arts and Science, Felician University, Rutherford, NJ 07070, USA
4
School of Design and Informatics, Abertay University, Dundee DD1 1HG, Scotland, UK
5
Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Selangor 53100, Malaysia
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(7), 1120; https://doi.org/10.3390/electronics9071120
Received: 6 May 2020 / Revised: 16 June 2020 / Accepted: 19 June 2020 / Published: 10 July 2020
(This article belongs to the Special Issue AI-Enabled Security and Privacy Mechanisms for IoT)
With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment. View Full-Text
Keywords: blockchain; Internet of Things; intrusion detection system; multi-agent system blockchain; Internet of Things; intrusion detection system; multi-agent system
Show Figures

Figure 1

MDPI and ACS Style

Liang, C.; Shanmugam, B.; Azam, S.; Karim, A.; Islam, A.; Zamani, M.; Kavianpour, S.; Idris, N.B. Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems. Electronics 2020, 9, 1120. https://doi.org/10.3390/electronics9071120

AMA Style

Liang C, Shanmugam B, Azam S, Karim A, Islam A, Zamani M, Kavianpour S, Idris NB. Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems. Electronics. 2020; 9(7):1120. https://doi.org/10.3390/electronics9071120

Chicago/Turabian Style

Liang, Chao; Shanmugam, Bharanidharan; Azam, Sami; Karim, Asif; Islam, Ashraful; Zamani, Mazdak; Kavianpour, Sanaz; Idris, Norbik B. 2020. "Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems" Electronics 9, no. 7: 1120. https://doi.org/10.3390/electronics9071120

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop